Simon J. D. Prince

Computer Vision and Pattern Recognition · 2007–2020

A researcher in the Science corpus: 5 papers; trusts 307 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 68 citations into it; #4,413 of 21,575 by trust here, #5,987 by citations
• Artificial Intelligence — 44 citations into it; #27,877 of 35,345 by trust here, #17,412 by citations
• Generative Models — 35 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it
• Statistical and Nonlinear Physics — 21 citations into it
• Signal Processing — 14 citations into it
• Model Architectures — 11 citations into it
• Statistics and Probability — 8 citations into it

Whom Simon J. D. Prince trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Peter N. Belhumeur
5. Kaiming He
6. Alex Krizhevsky
7. Max Welling
8. Radford M. Neal
9. Yann LeCun
10. Zoubin Ghahramani

Who trusts Simon J. D. Prince overall

1. Nikolaus Kriegeskorte
2. Daniel Garcia-Romero
3. Changxing Ding
4. Florin Curelaru
5. Ming Li
6. David Snyder
7. Michalis Vrigkas
8. Yevhenii Prokopalo
9. Weicheng Cai
10. Tomi H. Kinnunen

Papers from Simon J. D. Prince's perspective

Ordered by how strongly the researchers Simon J. D. Prince trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Superpixel lattices (2008) — 427 citations
• Normalizing Flows: An Introduction and Review of Current Methods (2020) — 1,546 citations
• Probabilistic Linear Discriminant Analysis for Inferences About Identity (2007) — 1,155 citations
• Modeling object appearance using Context-Conditioned Component Analysis (2015) — 5 citations
• Computer Vision: Models, Learning, and Inference (2012) — 539 citations